Data from: A method for detecting characteristic patterns in social interactions with an application to handover interactions

Social interactions are a defining behavioural trait of social animals. Discovering characteristic patterns in the display of such behaviour is one of the fundamental endeavours in behavioural biology and psychology, as this promises to facilitate the general understanding, classification, prediction and even automation of social interactions. We present a novel approach to study characteristic patterns, including both sequential and synchronous actions in social interactions. The key concept in our analysis is to represent social interactions as sequences of behavioural states and to focus on changes in behavioural states shown by individuals rather than on the duration for which they are displayed. We extend techniques from data mining and bioinformatics to detect frequent patterns in these sequences and to assess how these patterns vary across individuals or changes in interaction tasks. To illustrate our approach and to demonstrate its potential, we apply it to novel data on a simple physical interaction, where one person hands a cup to another person. Our findings advance the understanding of handover interactions, a benchmark scenario for social interactions. More generally, we suggest that our approach permits a general perspective for studying social interactions.

社会互动是社会性动物的标志性行为特征。挖掘此类行为表现中的特征模式，是行为生物学与心理学领域的核心研究方向之一，因其有助于推动对社会互动的全面理解、分类、预测乃至自动化实现。本研究提出一种用于研究社会互动中特征模式的全新方法，可同时覆盖序列动作与同步动作两类交互行为。本分析的核心思路是将社会互动表征为行为状态序列，并聚焦于个体展现出的行为状态变化，而非个体维持该状态的时长。我们拓展了数据挖掘与生物信息学领域的技术手段，用于检测此类序列中的频繁模式，并评估这些模式如何随个体差异或交互任务的变更而发生变化。为验证本方法并展示其应用潜力，我们将其应用于一项简单肢体交互的全新数据集，该数据集记录了一人向另一人递水杯的交互过程。本研究的发现深化了对交接互动的认知，而交接互动本身便是社会互动的基准场景之一。从更广泛的层面来看，本方法可为社会互动的研究提供普适性的分析视角。